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tutorial_machinelearning_matlab

A tutorial on machine learning from scratch with Matlab

tutorial_separation

This repo summarizes the tutorials, datasets, papers, codes and tools for speech separation and speaker extraction task. You are kindly invited to pull requests.

two-stage-speech-enhancement

Supplementary material for the paper "Speech Enhancement by LSTM-Based Noise Suppression Followed by CNN-Based Speech Restoration" by Maximilian Strake, Bruno Defraene, Kristoff Fluyt, Wouter Tirry, and Tim Fingscheidt.

twostreamfusion

Code release for "Convolutional Two-Stream Network Fusion for Video Action Recognition", CVPR 2016.

u2f-ref-code

U2F reference implementations

uaspeech

Baseline kaldi script for UA-SPEECH corpus

ublas

Development version of Boost uBLAS

uis-rnn

This is the library for the Unbounded Interleaved-State Recurrent Neural Network (UIS-RNN) algorithm, corresponding to the paper Fully Supervised Speaker Diarization.

ulc-codec

Ultra-low-complexity Audio Codec

unified2021

A UNIFIED SPEECH ENHANCEMENT FRONT-END FOR ONLINE DEREVERBERATION, ACOUSTIC ECHO CANCELLATION, AND SOURCE SEPARATION

unifiedudetdetbss

unsupervised-capsule-network

Capsule network with variations. Originally proposed by Tieleman & Hinton : http://www.cs.toronto.edu/~tijmen/tijmen_thesis.pdf

unsupervised-noise-classification

unsupervised-videos

Unsupervised Learning of Video Representations using LSTMs

unvoiced_sound_classification

Classification of unvoiced speech phones. Uses MATLAB.

uofe_dissertation

Independent Component Analysis (ICA) has received a lot of attention in statistical as well as in biomedical signal processing. It is widely used in blind source separation (BSS) problems, as it is a convenient method to separate signals from different sources, without any prior information about them or the mixing process. In the first part of the dissertation we report a theoretical background of ICA, analyzing what are the preprocessing steps that are needed and how ICA works, and then giving more details on the two algorithms that are compared, fastICA and ProDenICA. In the second part we present experimental results in a simulation environment to see what ICA achieves and what are the merits and drawbacks of the two ICA algorithms while in the third part we consider a real surface Electromyography (sEMG) dataset. sEMG is one type of bioelectrical signals produced by the human body and contain significant information about muscle activity. ICA is applied to sEMG signals in order to recover the original signals originating from each muscle. Besides, a post-ICA method that overcomes the independent component ordering ambiguity is proposed, allowing them to be related to the suitable corresponding muscles. ICA and the post-ICA steps that are described, manage to reach more than 79% accuracy on three hand gesture classification problems. The experimental results in both simulation and sEMG dataset indicate that ICA is an appropriate method for signal recovering and identification of hand gestures using sEMG signals.

upit-for-speech-separation

Speech separation with utterance-level PIT experiments

urban-sound-classification

Urban sound classification using Deep Learning

urban-sound-classification-1

Urban Sound Classification: With Random Forest, SVM, DNN, RNN, and CNN Classifiers

urban-sound-classification-keras

us-beamform-linarray

Ultrasound beamforming using a linear array in Python and Rust.

usb_4_mic_array

ReSpeaker 4 Mic Array with builtin VAD, DOA, AEC, Beamforming & NS

ustb

fork of ultrasound tool box. origin: https://bitbucket.org/ustb/ustb/src/master/

ustc-cs-courses-resource

:heart:**科学技术大学计算机学院课程资源(https://mbinary.xyz/ustc-cs/)

ustcthesis

LaTeX template for USTC thesis v2.0

vace_wpe

VACE-WPE

vad

vad-1

simple energy vad

vad-2

Voice activity detection (VAD) toolkit including DNN, bDNN, LSTM and ACAM based VAD. We also provide our directly recorded dataset.

vad-3

从webrtc抽离出来的vad源代码，供语音分析/检测使用